Carbon sequestration

We used  Sentinel-2 images to create a dataset In order to estimate Sequestered carbon in the above-ground forest Biomass. We also created three different sizes of training datasets. The new training datasets consisted of six qualitative categories (no carbon, very low, low, medium, high, and very high). The clipped image has a size of 1115 × 955 pixels and consists of bands 3, 4, and 8, which correspond to green, red, and near-infrared. These bands were selected for two reasons: they have the highest spatial resolution, and they are representative of the crops’ photosynthesis process.  We created different datasets that consisted of tiled sub-images with three different sizes of 32 × 32 (1050 images), 64 × 64 (270 images), and 128 × 128 (72 images) and three bands representing different spectrums (green, red, and near-infrared). The other datasets consisted of the same size and number of tiles but only represent carbon sequestration with six classes (no carbon, very low, low, moderate, high, and very high). A script was written in the Python language to classify the Sentinel-2 sub-images into six classes based on the computed canopy density statistics.